task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
10-shot image generation > Semantic Segmentation | DensePASS | Fast-SCNN | http://arxiv.org/abs/1902.04502v1 | mIoU | 24.6% |
10-shot image generation > Semantic Segmentation | DensePASS | PASS | https://arxiv.org/abs/1909.07721v2 | mIoU | 23.66% |
10-shot image generation > Semantic Segmentation | DensePASS | ERFNet | https://ieeexplore.ieee.org/abstract/document/8063438 | mIoU | 16.65% |
10-shot image generation > Semantic Segmentation | SIFT-flow | RBE2E | http://arxiv.org/abs/1607.07671v1 | Mean Accuracy | 64 |
10-shot image generation > Semantic Segmentation | SIFT-flow | RBE2E | http://arxiv.org/abs/1607.07671v1 | Pixel Accuracy | 84.3 |
10-shot image generation > Semantic Segmentation | SIFT-flow | JCSS | http://arxiv.org/abs/1507.01581v4 | Mean Accuracy | 59.2 |
10-shot image generation > Semantic Segmentation | SIFT-flow | JCSS (weakly supervised) | http://arxiv.org/abs/1507.01581v4 | Mean Accuracy | 44.8 |
10-shot image generation > Semantic Segmentation | CEMS-W | UPerNet (RN50) | https://arxiv.org/abs/2309.08368v1 | mIoU | 84.94 |
10-shot image generation > Semantic Segmentation | CEMS-W | SegFormer (MiT-B3) | https://arxiv.org/abs/2309.08368v1 | mIoU | 83.34 |
10-shot image generation > Semantic Segmentation | CEMS-W | UPerNet (ViT-S) | https://arxiv.org/abs/2309.08368v1 | mIoU | 82.98 |
10-shot image generation > Semantic Segmentation | US3D | DeepLabV3+ | http://arxiv.org/abs/1802.02611v3 | mIoU | 74.42 |
10-shot image generation > Semantic Segmentation | US3D | HRNet-48 | https://arxiv.org/abs/1908.07919v2 | mIoU | 72.66 |
10-shot image generation > Semantic Segmentation | US3D | HRNet-18 | https://arxiv.org/abs/1908.07919v2 | mIoU | 60.33 |
10-shot image generation > Semantic Segmentation | CamVid | SERNet-Former | https://arxiv.org/abs/2401.15741v7 | Mean IoU | 84.62 |
10-shot image generation > Semantic Segmentation | CamVid | SIW | https://arxiv.org/abs/2202.02002v2 | Mean IoU | 83.7 |
10-shot image generation > Semantic Segmentation | CamVid | DSNet-Base | https://arxiv.org/abs/2406.03702v1 | Mean IoU | 83.32 |
10-shot image generation > Semantic Segmentation | CamVid | RTFormer-Base | https://arxiv.org/abs/2210.07124v1 | Mean IoU | 82.5 |
10-shot image generation > Semantic Segmentation | CamVid | PIDNet-Wider | https://arxiv.org/abs/2206.02066v3 | Mean IoU | 82.0% |
10-shot image generation > Semantic Segmentation | CamVid | DeepLabV3Plus + SDCNetAug | https://arxiv.org/abs/1812.01593v3 | Mean IoU | 81.7 |
10-shot image generation > Semantic Segmentation | CamVid | DDRNet23 | https://arxiv.org/abs/2101.06085v2 | Mean IoU | 80.6% |
10-shot image generation > Semantic Segmentation | CamVid | ETC-Mobile | https://arxiv.org/abs/2002.11433v2 | Mean IoU | 76.3 |
10-shot image generation > Semantic Segmentation | CamVid | VideoGCRF | http://arxiv.org/abs/1807.03148v1 | Mean IoU | 75.2 |
10-shot image generation > Semantic Segmentation | CamVid | DenseDecoder | http://openaccess.thecvf.com/content_cvpr_2018/html/Bilinski_Dense_Decoder_Shortcut_CVPR_2018_paper.html | Mean IoU | 70.9 |
10-shot image generation > Semantic Segmentation | CamVid | BiSeNet | http://arxiv.org/abs/1808.00897v1 | Mean IoU | 68.7% |
10-shot image generation > Semantic Segmentation | CamVid | FC-DenseNet103 | http://arxiv.org/abs/1611.09326v3 | Global Accuracy | 91.5% |
10-shot image generation > Semantic Segmentation | CamVid | FC-DenseNet103 | http://arxiv.org/abs/1611.09326v3 | Mean IoU | 66.9% |
10-shot image generation > Semantic Segmentation | CamVid | EDANet | https://arxiv.org/abs/1809.06323v3 | Global Accuracy | 90.8 |
10-shot image generation > Semantic Segmentation | CamVid | EDANet | https://arxiv.org/abs/1809.06323v3 | Mean IoU | 66.4 |
10-shot image generation > Semantic Segmentation | CamVid | Dilated Convolutions | http://arxiv.org/abs/1511.07122v3 | Mean IoU | 65.3% |
10-shot image generation > Semantic Segmentation | CamVid | DFANet A | http://arxiv.org/abs/1904.02216v1 | Mean IoU | 64.7% |
10-shot image generation > Semantic Segmentation | CamVid | Template-Based NAS-arch0 (480x360 inputs) | https://arxiv.org/abs/1904.02365v2 | Mean IoU | 63.9% |
10-shot image generation > Semantic Segmentation | CamVid | LMDNet | http://arxiv.org/abs/1809.03994v1 | Mean IoU | 63.5 |
10-shot image generation > Semantic Segmentation | CamVid | Template-Based NAS-arch1 (480x360 inputs) | https://arxiv.org/abs/1904.02365v2 | Mean IoU | 63.2% |
10-shot image generation > Semantic Segmentation | CamVid | DeepLab-MSc-CRF-LargeFOV | http://arxiv.org/abs/1412.7062v4 | Mean IoU | 61.6% |
10-shot image generation > Semantic Segmentation | CamVid | ReSeg | http://arxiv.org/abs/1511.07053v3 | Global Accuracy | 88.7% |
10-shot image generation > Semantic Segmentation | CamVid | ReSeg | http://arxiv.org/abs/1511.07053v3 | Mean IoU | 58.8% |
10-shot image generation > Semantic Segmentation | CamVid | SegNet | http://arxiv.org/abs/1511.00561v3 | Mean IoU | 46.4% |
10-shot image generation > Semantic Segmentation | Mapillary val | AO-SegNet | https://ieeexplore.ieee.org/abstract/document/10176286 | mIoU | 76.0 |
10-shot image generation > Semantic Segmentation | Mapillary val | OneFormer (DiNAT-L, multi-scale) | https://arxiv.org/abs/2211.06220v2 | mIoU | 64.9 |
10-shot image generation > Semantic Segmentation | Mapillary val | Mask2Former (Swin-L, multiscale) | https://arxiv.org/abs/2112.01527v3 | mIoU | 64.7 |
10-shot image generation > Semantic Segmentation | Mapillary val | MaskFormer (ResNet-50) | https://arxiv.org/abs/2107.06278v2 | mIoU | 55.4 |
10-shot image generation > Semantic Segmentation | Mapillary val | NiseNet | https://ieeexplore.ieee.org/document/8803299 | mIoU | 48.32 |
10-shot image generation > Semantic Segmentation | Mapillary val | MRFP+(Ours) Resnet50 | https://arxiv.org/abs/2311.18331v2 | mIoU | 44.93 |
10-shot image generation > Semantic Segmentation | Mapillary val | SegBlocks-RN50 (t=0.4) | https://arxiv.org/abs/2011.12025v2 | mIoU | 39.7 |
10-shot image generation > Semantic Segmentation | Mapillary val | Resnet50 | https://arxiv.org/abs/2311.18331v2 | mIoU | 32.93 |
10-shot image generation > Semantic Segmentation | CC3M-TagMask | TTD (TCL) | https://arxiv.org/abs/2404.00384v2 | mIoU | 65.5 |
10-shot image generation > Semantic Segmentation | CC3M-TagMask | TCL | https://arxiv.org/abs/2212.00785v2 | mIoU | 60.4 |
10-shot image generation > Semantic Segmentation | CC3M-TagMask | TTD (MaskCLIP) | https://arxiv.org/abs/2404.00384v2 | mIoU | 50.2 |
10-shot image generation > Semantic Segmentation | CC3M-TagMask | MaskCLIP | https://arxiv.org/abs/2112.01071v2 | mIoU | 41.0 |
10-shot image generation > Semantic Segmentation | KITTI-360 | DiPFormer | https://arxiv.org/abs/2409.07995v1 | mIoU | 68.74 |
10-shot image generation > Semantic Segmentation | KITTI-360 | CMNeXt (RGB-D-E-LiDAR) | https://arxiv.org/abs/2303.01480v1 | mIoU | 67.84 |
10-shot image generation > Semantic Segmentation | KITTI-360 | HSPFormer-DBS(RGB-Depth) | https://doi.org/10.1109/TITS.2025.3525542 | mIoU | 67.32 |
10-shot image generation > Semantic Segmentation | KITTI-360 | HSPFormer-UFS(RGB) | https://doi.org/10.1109/TITS.2025.3525542 | mIoU | 66.82 |
10-shot image generation > Semantic Segmentation | KITTI-360 | CMX (RGB-Depth) | https://arxiv.org/abs/2203.04838v5 | mIoU | 64.43 |
10-shot image generation > Semantic Segmentation | KITTI-360 | CMX (RGB-LiDAR) | https://arxiv.org/abs/2203.04838v5 | mIoU | 64.31 |
10-shot image generation > Semantic Segmentation | KITTI-360 | ACNet (ResNet50) | https://arxiv.org/abs/1905.10089v1 | mIoU | 61.57 |
10-shot image generation > Semantic Segmentation | KITTI-360 | TokenFusion (RGB-Depth) | https://arxiv.org/abs/2204.08721v2 | mIoU | 57.44 |
10-shot image generation > Semantic Segmentation | KITTI-360 | ISSAFE (ResNet50) | https://arxiv.org/abs/2008.08974v2 | mIoU | 56.64 |
10-shot image generation > Semantic Segmentation | KITTI-360 | TransFuser (RGB-LiDAR) | https://arxiv.org/abs/2104.09224v1 | mIoU | 56.57 |
10-shot image generation > Semantic Segmentation | KITTI-360 | TokenFusion (RGB-LiDAR) | https://arxiv.org/abs/2204.08721v2 | mIoU | 54.55 |
10-shot image generation > Semantic Segmentation | KITTI-360 | PMF (RGB-LiDAR) | https://arxiv.org/abs/2106.15277v3 | mIoU | 54.48 |
10-shot image generation > Semantic Segmentation | KITTI-360 | ISSAFE (ResNet18) | https://arxiv.org/abs/2008.08974v2 | mIoU | 53.95 |
10-shot image generation > Semantic Segmentation | KITTI-360 | HRFuser (RGB-D-LiDAR) | https://arxiv.org/abs/2206.15157v3 | mIoU | 52.61 |
10-shot image generation > Semantic Segmentation | KITTI-360 | HRFuser (RGB-Depth) | https://arxiv.org/abs/2206.15157v3 | mIoU | 49.32 |
10-shot image generation > Semantic Segmentation | KITTI-360 | HRFuser (RGB-LiDAR) | https://arxiv.org/abs/2206.15157v3 | mIoU | 48.74 |
10-shot image generation > Semantic Segmentation | KITTI-360 | PGSNet (RGB-D-LiDAR) | http://openaccess.thecvf.com//content/CVPR2022/html/Mei_Glass_Segmentation_Using_Intensity_and_Spectral_Polarization_Cues_CVPR_2022_paper.html | mIoU | 48.51 |
10-shot image generation > Semantic Segmentation | THUD Robotic Dataset | SA-Gate | https://arxiv.org/abs/2007.09183v1 | mIoU | 83.19 |
10-shot image generation > Semantic Segmentation | THUD Robotic Dataset | ESANet | https://arxiv.org/abs/2011.06961v3 | mIoU | 78.42 |
10-shot image generation > Semantic Segmentation | THUD Robotic Dataset | RedNet | http://arxiv.org/abs/1806.01054v2 | mIoU | 76.92 |
10-shot image generation > Semantic Segmentation | THUD Robotic Dataset | ACNet | https://arxiv.org/abs/1905.10089v1 | mIoU | 74.83 |
10-shot image generation > Semantic Segmentation | ShapeNet | PatchFormer | https://arxiv.org/abs/2111.00207v3 | Mean IoU | 86.5% |
10-shot image generation > Semantic Segmentation | ShapeNet | SGPN | https://arxiv.org/abs/1711.08588v2 | Mean IoU | 85.8% |
10-shot image generation > Semantic Segmentation | ShapeNet | JSNet | https://arxiv.org/abs/1912.09654v1 | Mean IoU | 85.8% |
10-shot image generation > Semantic Segmentation | ShapeNet | Point-PlaneNet | https://www.sciencedirect.com/science/article/abs/pii/S1051200419301873 | Mean IoU | 85.1 |
10-shot image generation > Semantic Segmentation | ShapeNet | PointNet++ | http://arxiv.org/abs/1706.02413v1 | Mean IoU | 84.6% |
10-shot image generation > Semantic Segmentation | LOFAR RFI Detection | Nearest Latent Neighbours | https://arxiv.org/abs/2311.14303v2 | AUROC | 0.818 |
10-shot image generation > Semantic Segmentation | LOFAR RFI Detection | Nearest Latent Neighbours | https://arxiv.org/abs/2311.14303v2 | AUPRC | 0.414 |
10-shot image generation > Semantic Segmentation | LOFAR RFI Detection | Nearest Latent Neighbours | https://arxiv.org/abs/2311.14303v2 | F1 | 0.48 |
10-shot image generation > Semantic Segmentation | LOFAR RFI Detection | Spiking Nerest Latent Neighbours | https://arxiv.org/abs/2311.14303v2 | AUROC | 0.609 |
10-shot image generation > Semantic Segmentation | LOFAR RFI Detection | Spiking Nerest Latent Neighbours | https://arxiv.org/abs/2311.14303v2 | AUPRC | 0.321 |
10-shot image generation > Semantic Segmentation | LOFAR RFI Detection | Spiking Nerest Latent Neighbours | https://arxiv.org/abs/2311.14303v2 | F1 | 0.408 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic - RGBD | CBFC | https://arxiv.org/abs/2207.02437v1 | mIoU | 56.7 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic - RGBD | CBFC | https://arxiv.org/abs/2207.02437v1 | mAcc | 70.8 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic - RGBD | Tangent (ResNet-101) | https://arxiv.org/abs/1912.09390v3 | mIoU | 51.9 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic - RGBD | Tangent (ResNet-101) | https://arxiv.org/abs/1912.09390v3 | mAcc | 69.1 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic - RGBD | HoHoNet (ResNet-101) | https://arxiv.org/abs/2011.11498v3 | mIoU | 56.3 |
10-shot image generation > Semantic Segmentation | Stanford2D3D Panoramic - RGBD | HoHoNet (ResNet-101) | https://arxiv.org/abs/2011.11498v3 | mAcc | 68.9 |
10-shot image generation > Semantic Segmentation | SemanticPOSS | TFNet | https://arxiv.org/abs/2309.07849v3 | Mean IoU | 51.9 |
10-shot image generation > Semantic Segmentation | PASCAL VOC 2012 | DLDL-8s+CRF | http://arxiv.org/abs/1611.01731v2 | Mean IoU | 67.1 |
10-shot image generation > Semantic Segmentation | PASCAL VOC | SegCLIP | https://arxiv.org/abs/2211.14813v2 | mIoU | 52.6 |
10-shot image generation > Semantic Segmentation | Structured3D | SFSS-MMSI (RGB+Depth+Normal) | https://arxiv.org/abs/2308.09369v1 | Validation mIoU | 75.86 |
10-shot image generation > Semantic Segmentation | Structured3D | SFSS-MMSI (RGB+Depth+Normal) | https://arxiv.org/abs/2308.09369v1 | Test mIoU | 71.97 |
10-shot image generation > Semantic Segmentation | Structured3D | SFSS-MMSI (RGB+Normal) | https://arxiv.org/abs/2308.09369v1 | Validation mIoU | 74.38 |
10-shot image generation > Semantic Segmentation | Structured3D | SFSS-MMSI (RGB+Normal) | https://arxiv.org/abs/2308.09369v1 | Test mIoU | 71 |
10-shot image generation > Semantic Segmentation | Structured3D | SFSS-MMSI (RGB+Depth) | https://arxiv.org/abs/2308.09369v1 | Validation mIoU | 73.78 |
10-shot image generation > Semantic Segmentation | Structured3D | SFSS-MMSI (RGB+Depth) | https://arxiv.org/abs/2308.09369v1 | Test mIoU | 70.17 |
10-shot image generation > Semantic Segmentation | Structured3D | SFSS-MMSI (RGB Only) | https://arxiv.org/abs/2308.09369v1 | Validation mIoU | 71.94 |
10-shot image generation > Semantic Segmentation | Structured3D | SFSS-MMSI (RGB Only) | https://arxiv.org/abs/2308.09369v1 | Test mIoU | 68.34 |
10-shot image generation > Semantic Segmentation | Kvasir-Instrument | DoubleUNet | https://arxiv.org/abs/2006.04868v2 | DSC | 0.9038 |
10-shot image generation > Semantic Segmentation | Kvasir-Instrument | DoubleUNet | https://arxiv.org/abs/2006.04868v2 | mIoU | 0.8430 |
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